Senior Machine Learning Engineering Manager, Ad Platforms

Disney Disney · Media · Seattle, WA +2

Senior Machine Learning Engineering Manager for Ad Platforms at Disney, responsible for leading teams to build, enhance, and maintain high-performance, distributed, microservice-based advertising platforms. The role focuses on advancing AI/ML capabilities within ad serving, overseeing the end-to-end ML workflow from data collection to deployment, and ensuring responsible AI practices. Requires strong people management, technical leadership in ML/GenAI solutions, and experience with MLOps and cloud platforms.

What you'd actually do

  1. Lead, mentor and guide senior individual contributors and managers across data scientists, machine learning and AI engineers teams to build solutions adhering to industry best practices and deliver scalable solutions including model architecture and algorithm selection.
  2. Define strategic direction for machine learning projects and collaborate with product and engineering stakeholders.
  3. Drive adoption of best practices in model development, code quality, testing, and documentation.
  4. Oversee end-2-end machine learning workflow, including data collection, model development, deployment and modeling. These are expected to be aligned with the larger platform strategy and tools in collaboration with the global teams to stay consistent across Ad Platforms.
  5. Ensure responsible AI practices, including fairness, explainability, and compliance with privacy and ethical standards.

Skills

Required

  • People management
  • Machine learning fundamentals
  • Deep learning
  • Statistical modeling
  • Designing, building, and deploying scalable ML models and systems
  • Deploying ML/GenAI systems at scale
  • Cloud platforms
  • MLOps practices
  • Python
  • Java

Nice to have

  • GenAI solutions
  • model architecture
  • algorithm selection
  • data collection
  • model development
  • deployment
  • modeling

What the JD emphasized

  • managing senior ICs and managers
  • deploying scalable machine learning models and systems in production
  • MLOps practices
  • ad serving

Other signals

  • managing ML/AI teams
  • deploying ML/GenAI systems at scale
  • end-to-end ML workflow
  • ad platforms